For feature extractor, we use the WideResNet [1]} trained following previous work ([2]}). For each dataset, we train the feature extractor with base classes and test the performance using novel classes. Note that the feature representation is extracted from the penultimate layer (with a ReLU activation function) from the feature extractor, thus the values are all non-negative so that the inputs to Tukey's Ladder of Powers transformation in Equation REF  are valid. At the distribution calibration stage, we compute the base class statistics and transfer them to calibrate novel class distribution for each dataset.
We use the LR and SVM implementation of scikit-learn ([3]}) with the default settings. We use the same hyperparameter value for all datasets except for \(\alpha \) . Specifically, the number of generated features is 750; \(k=2\)  and \(\lambda =0.5\) . \(\alpha \)  is 0.21, 0.21 and 0.3 for miniImageNet, tieredImageNet and CUB, respectively.
